GLiNER
urchade/GLiNER
Generalist and Lightweight Model for Named Entity Recognition
Overview
A Python-based tool offering zero-shot named entity recognition, relation extraction, PII detection, information extraction, and token classification.
Categories
Tags
Similar tools
ECC
affaan-m/ECC
The agent harness performance optimization system
AutoGPT
Significant-Gravitas/AutoGPT
AutoGPT: Build, Deploy, and Run AI Agents
ollama
ollama/ollama
Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.
prompts.chat
f/prompts.chat
The world's largest open-source prompt library for AI
transformers
huggingface/transformers
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models
JavaGuide
Snailclimb/JavaGuide
Snailclimb/JavaGuide: 面试 & 后端通用面试指南，覆盖计算机基础、数据库、分布式、高并发、系统设计与 AI 应用开发
Install
pip install GLiNERREADME
[!IMPORTANT] 🚀 GLiNER2 is Now Available from Fastino Labs! A unified multi-task model for NER, Text Classification & Structured Data Extraction. Check out fastino-ai/GLiNER2 →
GLiNER: Generalist and Lightweight Model for Named Entity Recognition
Zero-shot NER | Relation Extraction | PII Detection | Information Extraction | Token Classification
<a href="https://urchade.github.io/GLiNER"><img src="https://img.shields.io/badge/Docs-GLiNER-blue" alt="GLiNER Documentation"></a>
<a href="https://arxiv.org/abs/2311.08526"><img src="https://img.shields.io/badge/arXiv-2311.08526-b31b1b.svg" alt="GLiNER Paper"></a>
<a href="https://colab.research.google.com/drive/1mhalKWzmfSTqMnR0wQBZvt9-ktTsATHB?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open GLiNER In Colab"></a>
<a href="https://github.com/urchade/GLiNER/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/github/license/urchade/GLiNER?color=blue"></a>
<br>
<a href="https://discord.gg/x7hQsjX2Kk"><img alt="GLiNER Community Discord" src="https://img.shields.io/badge/Discord-GLiNER%20Community-5865F2?logo=discord&logoColor=white"></a>
<a href="https://www.reddit.com/r/GLiNER/"><img src="https://img.shields.io/badge/Reddit-r%2FGLiNER-FF4500?logo=reddit&logoColor=white" alt="Reddit r/GLiNER"></a>
<a href="https://huggingface.co/spaces/urchade/gliner_mediumv2.1"><img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-sm.svg" alt="Open GLiNER In HF Spaces"></a>
<a href="https://huggingface.co/models?library=gliner&sort=trending"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-yellow" alt="HuggingFace Models"></a>
<br>
<a href="https://clickpy.clickhouse.com/dashboard/gliner"><img src="https://static.pepy.tech/badge/gliner" alt="GLiNER Downloads"></a>
<a href="https://github.com/urchade/GLiNER"><img alt="GLiNER GitHub stars" src="https://img.shields.io/github/stars/urchade/GLiNER?style=social"></a>
GLiNER is a framework for training and deploying small Named Entity Recognition (NER) models with zero-shot capabilities. In addition to traditional NER, it also supports joint entity and relation extraction, as well as multi-task token classification. GLiNER is fine-tunable, optimized to run on CPUs and consumer hardware, and has performance competitive with LLMs several times its size, like ChatGPT and UniNER.
Other tasks such as text classification, entity linking, and schema extraction are supported through projects in the Ecosystem.
Why GLiNER?
|
Zero-shot Recognition
Extract any entity type — no labeled data or task-specific training required |
Runs Anywhere
CPU, INT8 quantization, |
Millions of Labels
Bi-encoder pre-computes label embeddings, scaling to 100+ entity types without degradation |
|
NER + Relations
Build knowledge graphs in a single pass with the joint RelEx architecture |
PII Detection
State-of-the-art multilingual PII models covering major entity types across 100+ languages |
Fine-Tune in Minutes
Few-shot learning on small datasets — bring your own labels and |